{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "2 Physical GPUs, 2 Logical GPUs\n"
     ]
    }
   ],
   "source": [
    "import tensorflow as tf\n",
    "import graphgallery \n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "\n",
    "# Set if memory growth should be enabled for ALL `PhysicalDevice`.\n",
    "graphgallery.set_memory_growth()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'2.1.2'"
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "tf.__version__"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'0.4.0'"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "graphgallery.__version__"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Load the Datasets\n",
    "+ cora\n",
    "+ citeseer\n",
    "+ pubmed"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "from graphgallery.data import Planetoid\n",
    "\n",
    "# set `verbose=False` to avoid these printed tables\n",
    "data = Planetoid('cora', root=\"~/GraphData/datasets/\", verbose=False)\n",
    "graph = data.graph\n",
    "idx_train, idx_val, idx_test = data.split()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'citeseer', 'cora', 'pubmed'}"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.supported_datasets"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Training...\n",
      "100/100 [==============================] - 2s 21ms/step - loss: 1.3589 - acc: 0.8500 - val_loss: 1.6401 - val_acc: 0.7860 - time: 2.1421\n",
      "Testing...\n",
      "1/1 [==============================] - 0s 120ms/step - test_loss: 1.5903 - test_acc: 0.8390 - time: 0.1196\n",
      "Test loss 1.5903, Test accuracy 83.90%\n"
     ]
    }
   ],
   "source": [
    "from graphgallery.nn.models import DAGNN\n",
    "model = DAGNN(graph, K=10, attr_transform=\"normalize_attr\", device='GPU', seed=123)\n",
    "model.build()\n",
    "# train with validation\n",
    "his = model.train(idx_train, idx_val, verbose=1, epochs=100)\n",
    "# train without validation\n",
    "# his = model.train(idx_train, verbose=1, epochs=100)\n",
    "loss, accuracy = model.test(idx_test)\n",
    "print(f'Test loss {loss:.5}, Test accuracy {accuracy:.2%}')\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Show model summary"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Model: \"dagnn\"\n",
      "__________________________________________________________________________________________________\n",
      "Layer (type)                    Output Shape         Param #     Connected to                     \n",
      "==================================================================================================\n",
      "attr_matrix (InputLayer)        [(None, 1433)]       0                                            \n",
      "__________________________________________________________________________________________________\n",
      "dense (Dense)                   (None, 64)           91712       attr_matrix[0][0]                \n",
      "__________________________________________________________________________________________________\n",
      "dropout (Dropout)               (None, 64)           0           dense[0][0]                      \n",
      "__________________________________________________________________________________________________\n",
      "dense_1 (Dense)                 (None, 7)            448         dropout[0][0]                    \n",
      "__________________________________________________________________________________________________\n",
      "dropout_1 (Dropout)             (None, 7)            0           dense_1[0][0]                    \n",
      "__________________________________________________________________________________________________\n",
      "adj_matrix (InputLayer)         [(None, None)]       0                                            \n",
      "__________________________________________________________________________________________________\n",
      "prop_convolution (PropConvoluti (None, 7)            7           dropout_1[0][0]                  \n",
      "                                                                 adj_matrix[0][0]                 \n",
      "__________________________________________________________________________________________________\n",
      "node_index (InputLayer)         [(None,)]            0                                            \n",
      "__________________________________________________________________________________________________\n",
      "gather (Gather)                 (None, 7)            0           prop_convolution[0][0]           \n",
      "                                                                 node_index[0][0]                 \n",
      "==================================================================================================\n",
      "Total params: 92,167\n",
      "Trainable params: 92,167\n",
      "Non-trainable params: 0\n",
      "__________________________________________________________________________________________________\n"
     ]
    }
   ],
   "source": [
    "model.summary()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Visualization Training "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1080x360 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "with plt.style.context(['science', 'no-latex']):\n",
    "    fig, axes = plt.subplots(1, 2, figsize=(15, 5))\n",
    "    axes[0].plot(his.history['acc'], label='Train accuracy')\n",
    "    axes[0].plot(his.history['val_acc'], label='Val accuracy')\n",
    "    axes[0].legend()\n",
    "    axes[0].set_title('Accuracy')\n",
    "    axes[0].set_xlabel('Epochs')\n",
    "    axes[0].set_ylabel('Accuracy')\n",
    "\n",
    "\n",
    "    axes[1].plot(his.history['loss'], label='Training loss')\n",
    "    axes[1].plot(his.history['val_loss'], label='Validation loss')\n",
    "    axes[1].legend()\n",
    "    axes[1].set_title('Loss')\n",
    "    axes[1].set_xlabel('Epochs')\n",
    "    axes[1].set_ylabel('Loss')\n",
    "    \n",
    "    plt.autoscale(tight=True)\n",
    "    plt.show()    "
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.7.3"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 4
}
